The $1 Million Content Experiment: What Future of Creation Has X Validated with Real Money?

Original Author: David, TechFlow

In mid-January, X announced a $1 million prize pool to reward the best-performing long-form Articles on the platform.

Elon Musk personally retweeted to confirm it. The rules were simple: open only to U.S. users, original English articles over 1000 words, ranked primarily by exposure among U.S. paying users.

You probably remember that just days before this content incentive program was launched, personal growth blogger Dan Koe published an article titled "How to fix your entire life in 1 day," which garnered 170 million impressions, becoming the best-performing Article in X's history.

X clearly saw the traffic potential of long-form content and quickly followed up: lowering the threshold for the Articles feature, adjusting algorithm weights to prioritize long-form posts over short tweets, and announcing the million-dollar writing contest.

During the two-week contest period, tens of thousands of people participated.

The results were announced on February 4th, with a total prize pool of $2.15 million, more than double the promised amount. The champion received $1 million, the runner-up $500,000, along with a $250,000 "Creator Choice" award and four $100,000 honorable mentions.

The award situation is roughly as follows:

You can see Dan Koe is on the list again. However, his previous article on how to fix your life in a day had 170 million impressions, while the champion of this creation contest only had 45 million.

Viral hits are still hard to come by, but several of the winning articles are worth analyzing.

🏆 Champion: A "Small Account" with 90k Followers Takes $1 Million with a Self-Built Database

The champion @beaverd's article title translates to "Deloitte, a $74 Billion Cancer Spreading Across America." It's about the well-known consulting firm Deloitte.

This account currently has "only" 90,000 followers, which is considered a small account compared to the other winners, and it has no media affiliation or any endorsement beyond a blue checkmark.

His topic also doesn't touch on any trending buzzwords, but the issue he exposes is quite controversial: how Deloitte secured $74 billion in contracts from federal and state governments and then botched the projects.

Link here

Clicking in, you'll find this person really put in the work.

He built his own website called somaliscan.com, scraped millions of government invoice data points, and cross-referenced them one by one with audit reports and system failure records.

Then, using this firsthand data, he tells a series of shocking stories: California's unemployment benefits system defrauded of $32 billion, Tennessee's Medicaid system crashing leaving 250,000 children without coverage, a court IT modernization project burning through $1.9 billion and then being abandoned... covering a total of 25 states.

He also uncovered the revolving door between Deloitte executives and government officials, detailing who moved from Deloitte to which department and then approved which contracts back to the firm, listing names and amounts clearly.

One person built his own database, conducted his own research, and earned $1 million.

🥈 Runner-up: A 700k-follower Finance Macro Account Teaching How to Profit from Tariff Panic

The runner-up @KobeissiLetter is a familiar face in the macro finance circle, with 700k followers, long focused on U.S. economic policy and market volatility.

What his article does is also very direct: it breaks down Trump's playbook for imposing tariffs into a repeatable trading framework, titled "Trump's Tariff Playbook: An Operating Manual."

Since Trump often acts unpredictably, liking to announce extreme policies and threaten other countries but not always fully following through, some on Wall Street summarized this pattern as TACO, short for Trump Always Chickens Out.

TACO describes a recurring pattern:

Trump announces aggressive tariffs → market plummets → a few days later he backs down or delays → market rebounds.

Link here

What KobeissiLetter's article does is turn TACO from a meme into a time-stamped operating manual. Using tariff events from the past 12 months as samples, he breaks down a complete cycle template for you to trade according to the timeline.

For example, the White House releases fear-mongering news over the weekend, bargain-hunting funds enter mid-week, conciliatory signals are released the next weekend, and some agreement is reached within 2 to 4 weeks. He also continuously updates with follow-up posts at each step, telling you which stage you're at now, making it more like a serialized pre-research thread.

He also provides a more practical method: watch the U.S. 10-year Treasury yield. If this number breaks above 4.60%, Trump is likely to back down.

For the paying users on X who follow macro and trading, this is exactly what they want.

It doesn't discuss whether tariffs are good or make moral judgments; it just tells you what actions to take at what point to make money the next time this playbook is used.

🥉 Third Place (Creator Choice): DAN KOE with the Most Likes, Familiar Life Methodology

Dan Koe's contest entry, "How to Enter a State of Extreme Focus at Will," received 42,000 likes and 8,681 reposts, the highest numbers among all entries for both metrics. However, its impression count was only 11.04 million, less than a quarter of the champion's.

Strictly speaking, X didn't award him third place but set up a separate "Creator Choice" award worth $250,000.

This is understandable, as Dan Koe is "the person who inspired this contest." His viral article with 170 million impressions in early January directly showed X how high the traffic ceiling for long-form content could be.

Link here

We won't delve too much into the article itself; it's still that set of life growth methodologies. It roughly discusses how to gain focus, citing concepts like neuroscience and flow states for support and depth.

Although this article had the best engagement data, according to the contest's core rule of "U.S. paying user homepage impressions," it didn't rank high.

Why did the article with the best engagement have lower impressions? This discrepancy will be discussed later.

Honorable Mentions: $100k × 4

Nick Shirley, Josh Wolfe, Kaizen Asiedu, and Ryan Hall each received a $100,000 incentive. Their accounts cover public policy, geopolitics, history, and public safety, respectively.

Among them, Josh Wolfe is the co-founder of Lux Capital, a well-known venture capitalist, who also announced he would donate the prize money equally to four charities.

Since the original post did not list the specific articles by these four individuals, and due to time and energy constraints, we did not conduct further investigation. We also welcome everyone to supplement this information.

Some In-depth Observations

From the contest results, some patterns we can see are:

The Article with the Most Likes Had Only a Quarter of the Champion's Impressions

The most counterintuitive data from this contest is undoubtedly Dan Koe's.

42,000 likes, 8,681 reposts, 4,627 comments—the highest engagement metrics across the board. But the impression count was only 11.04 million, less than a quarter of champion @beaverd's. @beaverd's likes were 30,000, fewer than Dan Koe's.

If you've worked in social media operations, this data set feels off. Generally, higher engagement should lead the algorithm to promote it more, resulting in higher impressions.

However, X's contest calculation wasn't based on total impressions, but on "U.S. paying user homepage timeline impressions." This metric excludes all non-U.S. users, non-paying users, and visits from search and personal profiles.

Dan Koe writes about personal growth, an audience that is naturally more global, with many non-U.S. followers. @beaverd writes about how American taxpayers' money is being wasted by Deloitte, an audience naturally concentrated in the U.S. Under the same algorithmic recommendation mechanism, the "geographic concentration" of the content determines the level of this metric.

90k Followers Beat 900k Followers: Content Scarcity > Follower Base

Champion @beaverd had 90k followers before the contest. Runner-up @KobeissiLetter had 700k followers. Dan Koe had 900k followers.

If follower count determined impressions, the ranking should be reversed. But the actual results show that in X's Articles recommendation logic, the weight of follower base is far less significant than imagined.

@beaverd's win was key because he had something others didn't—content scarcity still played a decisive role.

This is completely different from traditional traffic logic. Big accounts rely on follower base and posting frequency, but in an algorithm-dominated distribution environment, "whether you have exclusive content" is more important than "how many followers you have."

You Need to Build Your Own Content "Hardware"

Taking a step back, the topics of these three winning articles are completely unrelated: one investigates government contracts, one teaches you to trade tariff waves, and one talks about how to focus.

In any content platform's categorization system, they wouldn't appear on the same list. But they share a common point: each has its own independent "hardware," in other words, you need a narrative framework.

@beaverd's hardware is a self-built database scraping government data; KobeissiLetter's hardware is a trading framework backtested over 12 months; and Dan Koe's hardware is a six-chapter methodology blending neuroscience and psychology, though it may seem profound, it's essentially common knowledge.

None of the winning articles were pure opinion pieces. They all require long-form space to carry information density, which is precisely the reason for the existence of the X Articles product format.

Another noteworthy fact is that among the eight winners, there is not a single traditional media outlet.

They are all independent creators. This isn't to say traditional media didn't participate, but under this contest format, personal accounts actually have an advantage.

Institutional media content is usually published on their own websites, with only links and summaries posted on social media. But Articles require the full content to be published within X, which is an awkward move for media outlets accustomed to off-site traffic redirection.

What is X Really Buying with $2.15 Million?

Returning to the platform itself.

X initially promised a $1 million incentive but ended up awarding $2.15 million. During the contest, they also made a series of supporting moves: expanding the Articles feature from creator accounts to all paying users, adjusting the algorithm to increase the recommendation weight of long-form content, and changing the scoring method to "U.S. paying user homepage impressions."

At such a high cost, the most direct reason is certainly that X needs original long-form content within the platform.

In the past, long-form content on X mostly relied on external links—Substack, Medium, personal blogs. Users would click and leave, with reading time and engagement data staying elsewhere. The goal of Articles is to keep this content within X, allowing users to read from start to finish without leaving.

Looking deeper, X has Grok. Training large language models requires high-quality long-text data, and the vast majority of content on X is 280-character short tweets. If Articles can continuously attract creators to produce in-depth long-form content, this material becomes training data for Grok.

Finally, paying user value.

The contest rules limiting the metric to "U.S. paying user homepage impressions" directly tell creators that their content should serve paying users.

This uses creators' content to support the paid subscription system, making paying users feel "my money is worth it because I can see in-depth content on my homepage that I can't find elsewhere."

From a content creator's perspective, we feel the era of pure opinion may be coming to an end.

This trend also applies to creators in the crypto space. The crypto industry is not short on opinions; countless people on X are shilling tokens, predicting prices, and commenting on regulations every day.

But there are few who, like @beaverd, build their own on-chain data analysis tools, or like KobeissiLetter, break down market cycles into repeatable trading playbooks.

Maintaining scarcity and independence, and producing consistently, is actually a very professional job and also a highly rewarding and positive-feedback endeavor.

We also hope to see more content from the Chinese-speaking community appear on such lists in the future.